How can you know when you've analyzed enough data for creative problem solving?

Definir un problema parece una actividad sencilla pero definitivamente no lo es. Cada dato relevante que verifiquemos a traves de la experimentacion debe aportar un detalle que permita mejorar la definicion. A este paso vamos a retornar innumerable cantidad de veces, incluso cuando tengamos la accion definitiva, la tenemos que testear con la definicion para entender si es exactamente el problema lo que estamos resolviendo.

- Understand your business problems and the solution it needs - Set SMART goals for your business analysis - Gather related data - Analyze and understand the data and how it can help solve your business problem to bring up a solution

In simple terms, a problem indicates a deficiency in the key factors considered during the design of a program. For instance, components A, B, C, and D are crucial to the project's success. If an issue arises initially, examining these components should readily uncover the problem, and rectifying these factors will resolve the issue. If these factors aren't defined initially and a problem arises, it should first be dissected based on the impactful factors, assigning weights to prioritize their impact. Lastly, a model is developed from this analysis, and positive results prompt corrective action, even if other factors are present, making them less relevant.

Defining the problem should be the first step. What value are you adding? What itch is being scratched? Once you can state the problem in a clear sentence you can move to defining your goals. This should always be done collaboratively with key stakeholders to test your assumptions. You can also make use of divergent and convergent thinking theories. Divergent thinking is a creative process of coming up with unique and creative ideas. Convergent thinking is a process that allows you to refine these goals. Once you have a clearer idea of the goals you can make use of the SMART goals tool to consolidate these goals and create a clear direction to achieve them.

Remember, problem-solving is a playground, not a prison. Dig deep, set sail with clear goals, and embrace the twists. I approach it based on the below - Always remember every problem statement has a human on the other end narrating it so enabling an environment where he/she can express their problem freely is very important. - Dont solve immediately wait and hear the problem and take your time to understand it clearly - Not every problem requires a solution - Map the process while understanding the problem - Problem statements and Data can give different stories so always validate the the problem statement with the data and check if the data provided is for the problem statement - What is the end goal Should always be in your mind

Arguably the hardest of all the steps. It takes a lot of time and mental effort to differentiate gold from dirt. "Relevant" is the key word here. In my experience, whenever you take on a data driven analysis, the best path you can take is to keep you first model as simple as possible. Pick the least information needed to make it work, to reach a point where it makes sense, and start adding details once you know you have a strong working model.

One of the main aims of collecting data is to test your assumptions. Data will either confirm these assumptions or completely challenge them, in which case it is back to the drawing board. It is important to speak to key stakeholders who are directly impacted by your project, product, service, or concept. They will enable you to see your blind spots.

Some argue that too much data can lead to information overload and stifle creativity. They believe that intuition and experience can sometimes be more valuable than data in generating innovative solutions.

The key word here is "relevant". The best method I have found to gather relevant data is to work backwards. What do you want to say about your problem? For example: Our customer’s greatest problem with our product is ____________. Of the ten most common issues directed towards our support team, customers care most about _______________. Knowing what you want to say helps identify, with precision, what gap in your knowledge you are trying to fill and what it would take to fill that gap (feasibility). These sentences can be used to craft your questions. Working backwards also clarifies what methods will be best to gather the type of data you want and clarify who you need to gather data from.

As you look and condense all of your data. 1. See if you see patterns or similarities 2. Ensure you are able to articulate the problem you are solving for your customers -can you see it in the data (not what you do, but problem you solve) 3. If your data includes market or consumer research, really listen to what they are saying their needs and problems are, and shift your marketing wording or product offering. 4. Listen to your team and ensure you take their perspective and data into consideration.

Assess whether you have taken into account a diverse range of perspectives and viewpoints. This can help ensure that you've considered different angles and potential solutions. If you find that you've been predominantly analyzing data from a single source or perspective, it may be beneficial to seek out additional viewpoints. Consider the time available for analysis and problem-solving. In some cases, you may need to make decisions based on the data you have within the given time frame. Balancing the need for comprehensive analysis with practical considerations is important.

Analysing data can be a lengthy process. It is important to take your time here. Often we see what we want to in the data and can for example ignore essential feedback. Make sure to access each piece of data carefully. Go back to the source of the data and test it again if necessary for clarification.

Collect diverse and relevant data sources to ensure comprehensive coverage. Utilize advanced analytics tools to extract meaningful patterns and insights. Embrace creativity by exploring unconventional data correlations and outliers. Foster a collaborative environment to encourage diverse perspectives in analysis. Iteratively refine your approach based on emerging patterns for effective problem-solving.

A análise dos dados é um fator super importante e essencial, pois através deles é que podemos definir estratégias e direcionar a equipe.

Personally I feel people do not take time often enough to consider the what, why and how questions up front. This is an important part of this step and the previous. Also keep in mind if and when you may need to present the data up or down the chain. Different people digest information in different ways. I always find it helpful to maintain visuals such as charts and graphs in addition to any other method you select.

Gather diverse data relevant to the problem at hand. Use analytical tools to extract meaningful insights from the data. Generate creative ideas by synthesizing the analyzed information. Evaluate ideas based on feasibility, impact, and alignment with goals. Iterate the process as needed to refine and improve the proposed solutions.

With a solid foundation of analyzed data, encourage a collaborative brainstorming session. Be intentionally inclusive, consider diverse perspectives, and generate ideas. In a recent product innovation project, we analyzed market trends and consumer feedback, leading to the generation of creative ideas for product features. Evaluating these ideas against the data-driven insights ensured alignment with market demands.

Naturally in analyzing your data ideas will start to form. Keep track of those ideas that pop up prior to this. But also be mindful of time management, scope creep and resources as you evaluate these ideas.

Deploy your solution in a controlled environment to ensure stability and functionality. Set up monitoring tools to track key performance indicators and detect any anomalies. Continuously collect and analyze relevant data to identify patterns and insights. Leverage creative problem-solving techniques to generate innovative solutions. Regularly review and refine your approach based on the evolving data and emerging challenges.

Implement the selected solution and continuously monitor its impact. In an operational efficiency enhancement project, after analyzing data on workflow processes, we implemented a streamlined project management system. Regular monitoring and feedback loops allowed us to fine-tune the solution based on real-time data, ensuring sustained improvements. This customer-centric approach helps to ensure you are delivering the right results on behalf of the customer and not letting bias or ego dictate the direction.

Determining when you've analyzed enough data for creative problem-solving involves finding a balance between thorough investigation and avoiding analysis paralysis. It's about reaching a point where you have sufficient information to understand the problem and generate innovative solutions without getting overwhelmed by excessive data. Trust your instincts and focus on actionable insights that directly contribute to addressing the core issues.

I think this should be a little more layered. More aptly, implementation should happen in phases and waves when possible. Especially since on release there is an opportunity to get new data. Especially when introducing new ideas to certain environments and ecosystems. If you can soft launch then or do a test launch DO IT!

Try this as well: Holistic Value: Look beyond quantitative data. Consider qualitative factors such as user experiences, customer feedback, and employee insights to gain a holistic understanding of the problem Benchmarking: Compare your data findings with industry benchmarks. This external perspective provides valuable context and helps validate the effectiveness of your creative problem-solving initiatives. In a recent market expansion project I defined our goal of increasing market share in a new region. Through data analysis, we identified customer segments with high growth potential. By using targeted marketing strategies for these segments, we not only achieved our goal but also uncovered opportunities for further market diversification

Many years ago, someone mentioned a term Analysis Paralysis. The concept of being a stuck up with too much data on your hands. One thing that someone solcing a problem creatively is the fact that data will never be enough. It's a problem that requires a creative solution. If previously existent data could help you with such a problem, it would not be a creative problem, it would be a statistic.

Several things to consider: -Is the problem really what we think it is? -Are we really implementing the new solutions or just implementing old solutions to fix new problems? - Are we developing new approaches to old problems to improve efficiency? Shifting our mindsets to step out of the box and come up with creative solutions is necessary. Try not to underestimate those workers who are probably dealing with the issues firsthand. They may have the most creative ideas to solve some of your issues!

Before defining a problem it is critical to understand the problem correctly. I use a technique I term as 'knock out' where I often write different problem statements. The ones that pass the test of probability, plausibility, viability, affordability and acceptability I keep in tact. The ones that don't are knocked off. This is especially useful when the problem is complex with no predetermined or predefined goals and measures of success.